Spårning av nanopartiklar med iSCAT och maskininlärning
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Typ
Examensarbete för kandidatexamen
Bachelor Thesis
Bachelor Thesis
Program
Publicerad
2024
Författare
Jamal, Aram
Muriqi, Kelmend
Andersson, Karl Junior
Ahlskog, Malte Ambjörn Emanuel
Berger, Adam
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This study explores the tracking of particles of size 100 nm from iSCAT microscopy in two and three
dimensions with machine learning and algorithm-based tracking methods. The study compares the
accuracy of particle position predictions in two dimensions between the LodeSTAR method and the
Radial Variance Transform algorithm (RVT), how these predictions influence the creation of partic le trajectories with MAGIK, and how the diffusivities of particles depend on the chosen detection
method. Furthermore, the three-dimensional LodeSTAR method is modified to train on synthesized
images of particles in varying depths in order to gain vertical prediction capabilites. The accuracy of
the LodeSTAR method in three dimensions is evaluated by comparing the diffusivity of predicted par ticle trajectories from experimental data with their theoretical values, as well as through the analysis
of trajectories and their covariances in the vertical dimension. The results for the two-dimensional
LodeSTAR model indicate that RVT – based on covariance analysis – yields detections that indicate
Brownian motion of the particles, but LodeSTAR performs better in terms of the number of correctly
predicted particles and tracking trajectories over longer time periods. Furthermore, comparisons of
diffusivity, covariance and particle tracings for three-dimensional detections suggest that the LodeS TAR method extracts information about particle depth, but that the detections lack accuracy. It is
suggested that improving the synthesized data to better capture the particle shape in a greater depth
range would yield more accurate results. In summary, the LodeSTAR method shows promising future
potential for tracking particles in three dimensions from iSCAT microscopy footage
Beskrivning
Ämne/nyckelord
iSCAT, LodeSTAR, MAGIK, djupinlärning, partikelspårning.